Machine Learning is a part of computer science and an interesting branch of AI. It works with algorithms and data to learn itself and can improve with experiences. Today's businesses like shopping websites, social media platforms, and customer-facing companies rely on machine learning to understand the customer's insight, build marketing strategies, run ad campaigns, and improve performances. The demand for machine learning engineers has also increased significantly in this decade, and if you have become one, our platform offers the best courses in AI and Machine Learning for Free. This comprehensive roadmap is your gateway to pursuing this magnificent career path.
What is ML engineering? Machine Learning engineers developed extensive systems that get huge amounts of data and use them to direct algorithms that can learn intellectual tasks and produce useful insights and projections. They managed a complete data science pipeline, supplying and developing data, building and training new models, and deploying them to production. To become a machine learning engineer you need to be a master in data visualization tools and to learn ML you don't need to join any machine learning classes Our platform provides top-notch machine learning courses for Free.
Different Job-Role After Learning ML Engineering Machine Learning (ML) is a worthwhile career option due to its remarkable growth worldwide. let's dive into the top job opportunities in Machine learning.
1. Robotics Engineer Robotics engineers build and maintain different types of AI-powered robots as per the requirements of various industries like assembly lines, Healthcare robot-aid surgeries, Humanoid robots made to perform other tasks, etc.
2. AI Software Developer AI software developers build applications, tools, and systems that consolidate AI technologies. They develop AI-driven aspects like chatbots, virtual assistants, or other intelligent systems replicating human behavior.
3. Data Scientist Data Scientists extract huge amounts of data from various platforms and analyze it to understand valuable insights. they use machine learning, data visualization tools, and statistical techniques to make data-driven decisions.
4. Machine Learning Engineer The responsibility of a Machine learning engineer is to design and implement algorithms that facilitate machines to learn and make decisions with the help of data. They build models and systems for tasks like image recognition, NLP, and recommendation systems.
5. NLP Engineer NLP engineers especially work on developing systems that can understand speech patterns, and different human languages so that people easily communicate with machines and technology. They developed language translation tools and applications, and chatbots.
6. Automation Engineers Automation Engineers work with various teams like the Engineering Team, Data management, Quality Assurance, and Management team. They create and maintain automated machine models and applications for various manufacturing industries to speed production, accuracy, and better performance.
ML Engineering Learning Roadmap To learn machine learning our platform provides the fundamental recommendations you need with this comprehensive roadmap and the top-notch AI machine learning courses for free!
1. Learn Python and R Programming Start your learning journey with programming languages like Python and R. These are essential languages for AI development.
2. Learn Statistics and Data Analysis To become an AI Engineer you need to Understand statistical concepts, credibility, and data analysis. These create the foundation for AI algorithms and models.
3. Data Visualization Learn Matplotlib, Plotly, Seaborn, or Tableau for data visualization. these are popular libraries in Python.
4. Understand Machine Learning Algorithms and Frameworks Dive deeper into machine learning. Learn the basics of supervised and unsupervised learning. Study scikit-learn and XGBoost.
5. Delve into Natural Language Processing (NLP) Natural Language Processing (NLP) is essential for chatbots, language translation, and sentiment analysis. study different libraries like NLTK and spaCy.
6. Explore Neural Networks and Deep Learning Explore neural networks, backpropagation, and deep learning structures (e.g., CNNs and RNNs). Learn TensorFlow and PyTorch are essential tools for AI Technology.
7. Understand MLOps To understand the machine learning, workflows, deployments, and lifecycle You should learn cloud services platforms like AWS, GCP, and Azure.
8. Stay Updated and Build a Network You must stay updated in this field, Read research papers, newsletters, and blogs, follow AI ML conferences and seminars, and connect with professionals.
Learning Path by Different Free Courses Deep Dive into the fascinating world of ML Engineering without breaking the bank, visit TopFreeCourse for world-class AI Machine Learning courses. Gain valuable knowledge with the global community of learners, and intensify your expertise in ML Engineering for FREE!
1. Google's Python Class Course Link :Google's Python Class Description : This free Python course is designed for people with some programming experience. It provides written materials, videos, and code exercises to practice Python coding. The course covers basic Python concepts like strings and lists and progresses to more advanced topics like working with text files and HTTP connections.
2. Introduction to Statistics in Python Course Link :Introduction to Statistics in Python Description : In this course, you'll discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate the correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to use Python to conduct a well-designed study to draw your own conclusions from data.
3. Data Visualization and Dashboards with Excel and Cognos Course Link :Data Visualization and Dashboards with Excel and Cognos Description : Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets. Explain the important role charts play in telling a data-driven story. Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts. Build and share interactive dashboards using Excel and Cognos Analytics.
4. Machine Learning with Python Course Link :Machine Learning with Python Description : In the Machine Learning with Python Course, you'll use the TensorFlow framework to build several neural networks and explore advanced techniques like natural language processing and reinforcement learning. You'll also dive into neural networks, and learn the principles behind how deep, recurrent, and convolutional neural networks work.
5. Introduction to Natural Language Processing Course Link :Introduction to Natural Language Processing Description : In this free NLP course, you'll learn the fundamentals of Natural Language Processing (NLP) and Python, covering data pre-processing, tokenization, stemming, lemmatization, and stopwords. Engage in hands-on sessions implementing these techniques in Python. Explore models like Bag of Words and TF-IDF, understand word embedding, and delve into Machine Learning, logistic regression, and sentiment analysis, including a TextBlob demo. Conclude with insights into U-Net, semantic segmentation, and their demonstrations. Enroll, complete the quiz, and earn a certificate, taking a step towards mastering NLP. Explore Great Learning’s Best AI Courses for more on emerging technologies.
6. Neural Networks and Deep Learning Course Link :Neural Networks and Deep Learning Description : You will learn the foundational concepts of neural networks and deep learning, understand the key technological trends in deep learning, build and apply fully connected deep neural networks, implement efficient (vectorized) neural networks, identify crucial parameters in neural network architecture, and successfully apply deep learning principles to your own applications. This course serves as a gateway to comprehending the capabilities and challenges of deep learning, providing you with the knowledge and skills to contribute to cutting-edge AI technology, advance your technical career, and confidently navigate the world of machine learning.
7. MLOps for Scaling TinyML Course Link :MLOps for Scaling TinyML Description : Know why and when deploying MLOps can help your (tiny) product or business Key MLOps platform features that you can deploy for your data science project. How to automate a MLOps life cycle Real-world examples and case studies of MLOps Platforms targeting tiny devices.
Conclusion With the revolution of AI technology in this era, ML Engineering has become ever more important to strengthen Artificial Intelligence. Leading companies are hiring machine learning professionals worldwide. AI is the future of humanity consequently machine learning technology has become highly sought-after, and pursuing a career as an ML engineer is very advantageous. To become an ML engineer our platform provides all the useful information you need, delves into our best ML and AI courses, and gains proficiency without paying a single penny. – Embark on your journey to mastering ML Engineering for Free Now!
